The HTTP job entry in Pentaho Data Integration (PDI) is specifically designed to interact with web servers using the HTTP or HTTPS protocol. It can be configured to perform a GET request to a specified URL, which is the standard method for retrieving or downloading files from a website. The entry allows you to define the target URL and specify a local file path where the downloaded content will be saved, directly fulfilling the requirement of the task.

B. Sqoop import: This job entry is used for transferring bulk data between Hadoop and structured datastores like relational databases, not for downloading files from a web server.

C. Get a files with FTP: This entry uses the File Transfer Protocol (FTP) to download files from an FTP server, which is a different protocol and server type than a standard website (HTTP/HTTPS).

D. Copy Files: This job entry is used for copying files and folders within accessible file systems (local or network shares), but it does not support web protocols like HTTP.

1. Hitachi Vantara - Pentaho Documentation: The official documentation describes the HTTP job entry's function. It states

"The HTTP job entry allows you to do a single HTTP call. You can specify the URL to call and a target file to save the result to."

Source: Pentaho Wiki

"HTTP (job entry)". (The Pentaho Wiki serves as a primary official documentation source for PDI components).

2. Hitachi Vantara - Pentaho Documentation: The documentation for the "Get a file with FTP" job entry confirms its specific purpose: "The Get a file with FTP job entry allows you to get files from a remote server using the File Transfer Protocol (FTP)." This distinguishes it from the HTTP protocol.

Source: Pentaho Wiki

"Get a file with FTP (job entry)".

3. Hitachi Vantara - Pentaho Documentation: The purpose of the "Copy Files" job entry is explicitly defined for file system operations: "The Copy Files job entry allows you to copy one or more files and/or folders to a destination folder."

Source: Pentaho Wiki

"Copy Files (job entry)".

To install Pentaho's Adaptive Execution Layer (AEL) for use with Spark on a production Hadoop cluster, two key steps are required. First, the Spark application builder tool (spark-app-builder.sh) must be run. This utility packages the AEL daemon and all its dependencies into a single distributable zip file (ael-daemon-spark.zip). Second, the AEL daemon must be configured to run in YARN mode. This is accomplished by setting the ael.spark.master property to yarn in the config.properties file, which allows AEL to leverage the cluster's YARN resource manager for running Spark jobs, a standard practice for production Hadoop environments.

B. Configuring the AEL daemon in Local Mode is for single-machine development or testing and does not utilize the distributed resources of a production Hadoop cluster.

C. Oozie is a workflow scheduler used to manage Hadoop jobs. It is not the tool used to perform the initial installation of the AEL daemon service itself.

1. Hitachi Vantara Documentation

Pentaho 9.1

"Set Up the Adaptive Execution Layer (AEL) to Run on Spark": This official guide details the installation process.

Reference for A: In the section "Package the AEL Daemon into a Zip File

" the documentation states: "Run the Spark application builder tool to create the ael-daemon-spark.zip file..." This directly supports running the builder tool to obtain the daemon zip file.

Reference for D: In the section "Configure the AEL Daemon

" the documentation instructs users to edit the config.properties file and specifies: "Set the ael.spark.master property to yarn." This confirms the requirement to configure AEL for YARN mode on a cluster.

Reference for B & C: The same document outlines the installation process which does not involve an Oozie job. It also presents 'local' mode as an alternative to 'yarn' mode

clarifying its use for non-cluster environments

making it unsuitable for the production scenario described.

The Repository Explorer in Pentaho Data Integration (PDI) is the primary interface for managing content stored within the Pentaho Repository. Its core purpose is to provide file management and version control functionalities. Users can perform standard organizational tasks such as moving, renaming, and deleting repository objects like jobs and transformations. Additionally, to support multi-user collaboration and prevent conflicting edits, the Repository Explorer allows users to manually lock and unlock these assets. Creating new jobs/transformations is done in the main design canvas, and managing JDBC drivers is a separate configuration task.

A. New jobs and transformations are created using the "File > New" menu in the main Spoon application canvas, not directly within the Repository Explorer pane.

C. JDBC drivers are not uploaded via the Repository Explorer. They must be manually placed in the appropriate library directory (e.g., .../data-integration/lib) of the PDI client.

1. Hitachi Vantara Documentation - Work with the Pentaho Repository in PDI: This official guide details the functionalities of the Repository Explorer.

For option B (Move

Rename

Delete): The sections "Move Files and Folders

" "Rename Files and Folders

" and "Delete Files and Folders" explicitly describe these capabilities.

For option D (Lock

Unlock): The section "Lock and Unlock Files" states

"When you open a file from the Pentaho Repository

it is automatically locked... You can also manually lock and unlock files."

For option A (Create): The document shows that creating new content is initiated from the main "File" menu

not the explorer.

2. Hitachi Vantara Documentation - Work with Database Connections: This document explains the prerequisite for database connectivity.

For option C (JDBC Drivers): The section "Prerequisites" clearly states

"The appropriate database driver for the connection must be installed in the .../data-integration/lib folder for the PDI client..." This confirms driver management is a file-system-level task

not a Repository Explorer function.

To simplify migration, connection details must be externalized from the transformation (KTR) and job (KJB) files. This allows the same PDI solution to run in different environments (e.g., development, production) by simply changing the external configuration.

Method D involves defining connection parameters (like hostname, username, password) as variables within the central kettle.properties file. PDI automatically loads these variables at startup, making them globally available.

Method B uses a custom properties file for connection details. A parent job reads this file using a step like "Property Input" and then uses the "Set Variables" step to make them available to sub-jobs and transformations. This provides granular control over configuration loading.

Both methods effectively decouple the environment-specific details from the ETL logic.

A. Use the Data Source Wizard to generate the connection details.

This describes a UI tool for creating connections, not the externalization method itself. The connection created could be hard-coded or use variables, so this action alone does not guarantee externalization.

C. Use Manage Data Source to create a new connection in Spoon.

This is another UI action, functionally similar to the Data Source Wizard. It describes how a connection is created in the client, not the underlying strategy for making it portable between environments.

1. Hitachi Vantara Pentaho Documentation

"Use Variables to Streamline Your Processes": This document details the use of variables for parameterizing PDI processes. It explicitly describes the kettle.properties file as the location for defining variables that are global to the PDI environment. This supports using variables in database connections to abstract environment-specific details. (Reference supports option D).

2. Hitachi Vantara Pentaho Documentation

"Set Variables" step reference: The official documentation for the "Set Variables" step explains its function in setting variables dynamically within a job's scope. This is the core component of the pattern described in option B

where a job reads an external properties file and activates those settings for subsequent execution. (Reference supports option B).

3. Hitachi Vantara Pentaho Documentation

"Best Practices - Parameterize Everything": Pentaho best practices strongly advocate for parameterization to support environment migration. They outline the primary methods

including using the kettle.properties file for static environment settings and passing variables/parameters into jobs for more dynamic configurations

which aligns with options D and B.

In Pentaho Data Integration (PDI), for a variable to be substituted in the metadata of a step (like the SQL query in a Table input step), it must be defined in a scope accessible when the transformation initializes. The standard and correct method is to set the variable in the parent job before the transformation entry is executed. Using a "Set Variables" job entry accomplishes this. The job sets the variable, and the subsequent transformation inherits it, allowing the $(tablename) reference to be correctly resolved when the Table input step is prepared for execution.

A. The Get Variables step is used within a transformation to read variable values into the data stream as fields, not to set variables for metadata substitution in other steps.

C. The ? character is a parameter marker for prepared statements. It is used to safely insert data values (e.g., in a WHERE clause), not for dynamic SQL identifiers like table or column names.

D. A Set Variables step inside a transformation runs as part of the data flow. The Table input step is initialized before any rows are processed, so it cannot use a variable set by another step in the same data flow.

1. Hitachi Vantara Documentation

Pentaho Data Integration Concepts

"Variables": This documentation explains variable scope and inheritance. It clarifies that variables set in a job are passed down to the transformations and jobs that it executes. This principle supports setting the variable at the job level (Option B) to make it available to the transformation.

Reference: See the section on "Variable Scopes and Types" in the official Pentaho Data Integration documentation. It details how variables set with "Valid in the parent job" or "Valid in the root job" scopes are inherited.

2. Hitachi Vantara Documentation

PDI Job Entry Reference

"Set Variables": The documentation for the "Set Variables" job entry explicitly states its purpose is to define variables that can be used by subsequent entries within the same job. This directly aligns with the correct pattern described in Option B.

Reference: Official Hitachi Vantara Pentaho documentation for the "Set Variables" job entry.

3. Hitachi Vantara Documentation

PDI Transformation Step Reference

"Table Input": The documentation for this step specifies that variable substitution (using ${...} or $(...)) occurs when the "Replace variables in script?" option is enabled. This substitution happens during the initialization phase of the step

before data flow begins

reinforcing why the variable must be set beforehand (as in Option B) and not during the flow (as in Option D).

Reference: Official Hitachi Vantara Pentaho documentation for the "Table Input" transformation step.